Enhancing mobile crowd sensing with targeted regional incentives

Efficient budget allocation is essential for sustaining mobile crowdsensing systems (MCS), where resource distribution must balance cost-effectiveness and participant motivation to ensure optimal task execution. This paper introduces two novel winner selection mechanisms designed to maximize system rewards while adhering to financial constraints: a) Targeted Regional Incentive (TRI), and b) TRI with Critical Value (TRI-CV). The proposed incentive mechanisms are structured through regional divisions within the area of interest for effective task allocation. TRI employs a computationally efficient, low-overlap prioritization strategy that supports real-time task allocation across spatial regions. TRI-CV builds on this foundation by introducing region-specific critical value thresholds and a lightweight audit reassignment step, which reallocates budget from overrepresented to underserved regions to enhance spatial coverage and fairness. Simulation results show that TRI improves platform utility by 45–50% and reduces auction costs by 35–36%, while TRI-CV achieves 50–58% higher utility and 40–42% lower costs compared to established mechanisms such as FIRE, GRUS, and BFM. Additionally, TRI-CV demonstrates superior participant retention and scalability, positioning it as a robust and fairness-aware solution for dynamic Mobile Crowd Sensing environments.